Crash Scene Research Articles

Prehospital factors associated with mortality among road traffic injury patients: analysis of Cameroon trauma registry data

BackgroundCameroon is amongst the worst affected countries by road traffic injuries with an estimated 1443 disability-adjusted life years per 100,000 population. There have been very limited reports on the crucial prehospital response to road traffic injuries in Cameroon. This study aimed to identify prehospital factors associated with RTI mortality in Cameroon.MethodsWe included patients enrolled between June 2022 and March 2023 in the Cameroon Trauma Registry. Information about prehospital factors and demographic data was obtained from patients or their proxies. We examined the association of prehospital care factors like care at the crash scene and type of transportation during crash with final patient outcome. We used Chi-squared test to investigate the association between selected independent variables and mortality. A multivariable logistic regression model was built to identify independent predictors of dying from an RTI.ResultsRTIs constituted 69.5% (n = 3203) of all injuries in the Cameroon Trauma Registry. Only 20.7% (n = 102) of 4 + wheel vehicle occupants had seatbelts on and just 2.7% (n = 53) of motorcycle riders were wearing helmets during the collision. Only 4.9% (n = 156) of patients received any form of scene care. In-hospital mortality was 4.3% (n = 139) and was associated with male sex (AOR = 1.7, 95%CI = 1.08–2.80), crashing on a motorcycle (AOR = 2.08, 95%CI = 1.1–3.67) and scene care (AOR = 0.25, 95%CI = 0.04–0.80).ConclusionsReceiving any type of care at the scene such as bleeding control or being placed in the recovery position by bystanders is associated with improved survival. Improving on existing informal prehospital care responses should be a priority in Cameroon.

Congruency of Crash- and Hospital- Reported Injuries Among Child Passengers

Introduction: Prior work has found incongruencies in injury information reported by crash and hospital records. However, no work has focused on child passengers. The objective of this study was to compare crash scene and hospital-reported injury information for crash-involved child passengers. This study also explored injury location and severity by child age and restraint type. Methods: Utilizing linked New Jersey data from 2017 through 2019, the authors identified crash-involved child passengers <13 years old and their injuries in crash and hospital reports. Then, they characterized the congruency of injury frequency, severity, and location, as well as the frequency of injuries by child age and restraint type. Analyses were conducted from December 2023 through February 2024. Results: Of 84,060 crash-involved child passengers, crash reports documented 7,858 (9%) children with at least “possible” injuries, while 2,577 (3%) had at least one injury in hospital events. Crash report and hospital data were incongruent for both body region of injury and injury severity. The proportion of children injured increased as children's ages increased and as restraint type progressed. Conclusions: Crash reports overestimated the number of injured child passengers and misrepresented injury severity and locations. Child restraint systems mitigated a child's injury risk. Importantly, injury information documented on crash reports currently informs the allocation of traffic safety resources. These results highlight the importance of improving these reports’ accuracy and underscore calls to link administrative datasets for public health efforts.

Assessing the factors associated with pedestrian injury severity in hit-and-run crashes in Ghana

ABSTRACT Hit-and-run crashes often have severe consequences for vulnerable road users. In light of governmental efforts to promote pedestrian-friendly urban environments, the significance of these crashes cannot be overstated. In this study, we assessed the factors that influence the injury severity of pedestrians involved in hit-and-run crashes in Ghana. Historical crash data (1469 observations) spanning from 2013 to 2020 was used in this study. An injury-severity model was developed using random parameters logit approach to assess what crash factors significantly affect the injury outcome of the crashes. It was observed that hit-and-run crashes that occur on dark and unlit roadways were more likely to result in fatal injuries. Also, female pedestrians were less likely to be killed in hit-and-run crashes. These findings provide the basis for developing and implementing appropriate countermeasures, such as punitive laws for drivers who leave the crash scene and protective laws for those who help their victims.

SDC-Net++: End-to-End Crash Detection and Action Control for Self-Driving Car Deep-IoT-Based System.

Few prior works study self-driving cars by deep learning with IoT collaboration. SDC-Net, which is an end-to-end multitask self-driving car camera cocoon IoT-based system, is one of the research areas that tackles this direction. However, by design, SDC-Net is not able to identify the accident locations; it only classifies whether a scene is a crash scene or not. In this work, we introduce an enhanced design for the SDC-Net system by (1) replacing the classification network with a detection one, (2) adapting our benchmark dataset labels built on the CARLA simulator to include the vehicles' bounding boxes while keeping the same training, validation, and testing samples, and (3) modifying the shared information via IoT to include the accident location. We keep the same path planning and automatic emergency braking network, the digital automation platform, and the input representations to formulate the comparative study. The SDC-Net++ system is proposed to (1) output the relevant control actions, especially in case of accidents: accelerate, decelerate, maneuver, and brake, and (2) share the most critical information to the connected vehicles via IoT, especially the accident locations. A comparative study is also conducted between SDC-Net and SDC-Net++ with the same input representations: front camera only, panorama and bird's eye views, and with single-task networks, crash avoidance only, and multitask networks. The multitask network with a BEV input representation outperforms the nearest representation in precision, recall, f1-score, and accuracy by more than 15.134%, 12.046%, 13.593%, and 5%, respectively. The SDC-Net++ multitask network with BEV outperforms SDC-Net multitask with BEV in precision, recall, f1-score, accuracy, and average MSE by more than 2.201%, 2.8%, 2.505%, 2%, and 18.677%, respectively.

Critical patterns associated with vehicle-pedestrian hit-and-run casualty injury severity under different weather conditions: An association rule mining approach

Vehicle-pedestrian hit-and-run crashes have grave consequences as the pedestrian casualty is left at the crash scene, leading to delays in emergency response. Research indicates that prevailing weather conditions are pivotal in influencing these crashes. Nonetheless, there exists a substantial gap in understanding the diverse associations of contributing factors that impact the severity of pedestrian injuries in vehicle-pedestrian hit-and-run crashes under varying weather conditions. This paper extensively examines the intricate patterns of critical factor associations influencing the pedestrian casualty injury severity in vehicle-pedestrian hit-and-run collisions in Ghana, differentiating between clear and inclement weather conditions. Leveraging historical crash data, this research employs the robust Association Rule Mining tool to unveil the relationships between key crash factors and their outcomes. The association rules extracted from the data reveal that specific factors consistently influence injury outcomes in vehicle-pedestrian hit-and-run crashes, regardless of weather conditions. However, there are distinct patterns of factors contributing to fatal and severe injuries in these crashes that vary depending on the prevailing weather conditions. Hit-and-run crashes resulting in fatal and severe pedestrian injuries are primarily associated with males and are notably correlated with factors like pedestrian crossings, late and early morning hours, the absence of traffic control measures on median-separated motorways, and good urban roads, regardless of prevailing weather conditions. During clear weather conditions, fatal/severe injuries tend to occur in daylight conditions, primarily on roadways devoid of shoulders, and involve older pedestrians crossing the road. Conversely, for inclement weather conditions, these crashes are more prone to occur during nighttime hours on unlit roads, often involving younger pedestrians walking along the road's edge. The reduction of fatal/severe injury hit-and-run crashes involving vehicles and pedestrians can be effectively tackled by eliminating or improving the critical factors pinpointed in the extracted rules. The study underscores the urgency of policy recommendations to mitigate fatalities resulting from vehicle-pedestrian hit-and-run crashes, emphasising the need for stricter legislation, heightened enforcement measures, increased awareness of pedestrian safety, enhanced driver training, and infrastructure upgrades. By acting on these insights, a significant stride can be made in curbing the alarming rates of vehicle-pedestrian hit-and-run fatalities.

Exploring Uncertainties for Crashed Vehicle Travelling Velocity Tolerance in One-Dimensional Crash Configuration

Evidence suggests that measurement uncertainty is among the most important factors for reliability assessment. Uncertainty plays a vital role in maximising velocity calculation accuracy in crash reconstruction works. It serves as a reliable calculation tolerance, whereby its contribution becomes more significant when the calculated initial velocity is nearly approaching the posted speed limit. The results suggested that damage width and midpoint offset have a low sensitivity of Delta-V from 0.5 km/h to 0.75 km/h for up to 80 cm and 60 cm measurement errors, respectively. For the crush profile variable, a lesser measurement error of 8 cm results in 0.9 km/h deviation in Delta-V. Meanwhile, vehicle mass, drag factor and post-impact displacement have a higher sensitivity of Delta-V, as compared to the two previous variables. The calculation results may deviate from the actual figure for 1 km/h with a missing 75 kg adult occupant. Deviation of almost 2 km/h initial velocity was observed for as low as 0.5 drag factor determination fault. Moreover, with 1 m displacement measurement inaccuracy is also giving rise to the resulting initial speed of 2 km/h. Overall, the principle direction of force recorded the greatest velocity sensitivity among the investigated variables. Within 20o and 45o of inaccurate principle direction of force, the Delta-V deviation increases exponentially, up to 4.25 km/h. The presented findings are beneficial in terms of crash investigator judgment for the thoroughness measurement while conducting the assessment of the crashed vehicle and the crash scene. This investigation will also contribute to enhancing our understanding of tolerance determination for more accurate velocity estimation.

Modeling the influence of connected vehicles on driving behaviors and safety outcomes in highway crash scenarios across varied weather conditions: A multigroup structural equation modeling analysis using a driving simulator experiment

Equipped with advanced sensors and capable of relaying safety messages to drivers, connected vehicles (CVs) hold the potential to reduce crashes. The goal of this study is to assess the impacts of CV technologies on driving behaviors and safety outcomes in highway crash scenarios under diverse weather conditions, including clear and foggy weather. A driving simulator experiment was conducted and the multigroup structural equation modeling (SEM) was employed to explore the complex interrelationships between the propensity of traffic conflicts, utilization of CV alerts, weather, psychological factors, driving behaviors, and other relevant variables for two different crash locations, namely a straight section and a horizontal curve. Two latent psychological factors including aggressiveness and unawareness were constructed from driving behavior as vehicles passed by crash scenes such as brake, throttle, steering angle, lane offset, and yaw. The SEM can measure latent psychological factors and model interrelationships concurrently through a single statistical estimation procedure. Results of the multigroup SEM showed that CV alerts could significantly reduce the unawareness on a horizontal curve and thus lower the propensity of traffic conflicts. Additionally, the overall effect of foggy weather on conflicts was found to be positive on a horizontal curve, despite the potential benefit of improving situational awareness. In contrast, the single group SEM failed to reveal any significant interrelationships in its structural model by pooling data from both crash locations. The obtained insights can guide the development of driving assistance systems, highlighting the necessity of customization considering weather conditions and location-specific factors.

Assessing the effects of geometry and non-geometry related factors in work-zone crashes

Objective Work zones are unique in geometry and traffic management, utilizing special traffic signs, standard channelizing devices, appropriate barriers, and pavement markings. These configurations can introduce unexpected driving conditions, potentially posing risks to drivers. This analysis aims to explore potential differences in contributing factors between work-zone crashes where geometry was identified as a factor and those where it was non-geometry factor. To gain insights into driver injury severities in single-vehicle work-zone crashes, this study analyzed work zone crash data from Florida. Method This study employed random parameters logit models, accommodating potential variations in parameter estimates’ means and variances. The dataset encompassed a wide array of factors known to influence driver injury severity, encompassing crash characteristics, vehicle attributes, roadway features, prevailing traffic volume, driver profiles, and spatial and temporal considerations. Results This analysis yielded significantly distinct parameters for work-zone crashes, distinguishing between geometry-related and non-geometry-related factors (primarily the human factors). This distinction suggests a complex interplay between these factors. Notably, the marginal effects of individual parameter estimates exhibited marked differences between these two categories – geometry and non-geometry factors. Conclusion These findings contribute to the growing body of research indicating that geometric restrictions within work zones introduce a distinct set of risk factors compared to non-geometry-related factors. Recognizing the significance of geometric restrictions, beyond typical driving conditions, holds the implications for enhancing safety within various work zone configurations and offers valuable insights for crash scene investigators to pinpoint contributing factors accurately.

Profiles of pregnant women encountering motor vehicle crashes in Taiwan, 2008–2017

BackgroundUnderstanding demographic profiles is essential to the assessment of health burden imposed by motor vehicle crashes (MVCs) on pregnant women. However, Asian studies that have examined it are lacking. The study aimed to describe the demographic characteristics and prevalence of MVCs involving pregnant women in Taiwan.MethodsA cross-sectional study conducted by the Taiwan Birth Notification dataset from 2008 to 2017 was linked with the police-reported traffic collision registry to identify pregnant women involved in MVCs. The pregnant women were categorized according to their gestational age, age at delivery, the role of road user (driver, passenger, or pedestrian), and vehicle types (car, two-wheeled motor vehicle, or others). A chi-square test was performed for the significance test.ResultsA total of 22,134 (1.13%) pregnant women were involved in MVCs in the study period. Two-wheeled motor vehicle (47.9%) and driver (81.4%) were the mainly reported vehicle type and road user at the crash scenes, respectively. The majority of MVCs occurred in pregnant women aged 28–34 years. The number of MVCs rapidly declined after 37 weeks of gestation, especially two-wheeled motor vehicle or car crashes. However, the number of pedestrian victims climbed up during the third trimester.ConclusionPregnant women are susceptible to MVCs regardless of their gestational age, role of a road user, or type of vehicle. The findings of this study emphasize the need for increased awareness of traffic collision prevention among pregnant women aged 28–34. In addition, improving pedestrian safety is essential for the reduction of pregnant victims.

Assessing Precision and Dependability of Reconstructed Three-Dimensional Modeling for Vehicles at Crash Scenes using Unmanned Aircraft System

This study focuses on the accuracy assessment of 3D reconstructions of crime scenes using Unmanned Aircraft Systems (UAS) and Terrestrial Laser Scanners (TLS) data for forensic crash investigation. Forensic crash investigation involves meticulously analyzing physical evidence, vehicles, and human factors in road collisions to determine the sequence of events. Preserving the original state of the crash scene before cleaning is essential for accurate forensic analysis. However, this preservation process can disrupt normal activities and demand considerable time. Geomatic technology, specifically UAS or drones, offers a potential solution for efficient and precise forensic mapping. The application of UAS technology enables swift data collection, leading to cost savings, enhanced safety, and data utilization. This study aims to assess the suitability of UAS techniques for forensic mapping, encompassing both relative and absolute accuracy. This research uses a UAS to rapidly and comprehensively capture evidence from a simulated crash site using predefined flight paths. The acquired image data is then processed utilizing Agisoft Metashape software, generating a detailed 3D model of the crash scene. This model can be enriched with annotations, measurements, and pertinent information. A comparative analysis is performed by preparing a table that contrasts the absolute and relative accuracy of UAS-collected data with that obtained from TLS, which serves as a benchmark. The results reveal that the UAS demonstrates a relative accuracy Root Mean Square Error (RMSE) of approximately ±4.1 cm compared to TLS. Concerning absolute precision, the UAS-produced RMSE values are determined as ±0.20719 for the X coordinate, ±0.164 for the Y coordinate, and ±0.001584 for the Z coordinate compared to GNSS data, which functions as the benchmark. The utilization of UAS technology offers a non-invasive measurement approach that eliminates direct physical contact between the operator and the documented object. This non-intrusive method ensures the preservation of the original scene characteristics and has shown its superiority over conventional approaches in managing crash scenes. Overall, this study underscores the potential of UAS technology in accurately reconstructing crime scenes for forensic investigation purposes.

Examining traffic crash scene using density estimation and its relevance to determine intersection zone road network blackspot location

Identifying road traffic crash blackspot locations and the proper method of assessment is a step towards reducing traffic crashes. The objective of this study was assessing the pertinence of the point density estimation and kernel density estimation in identifying blackspot locations using the ArcGIS tool. Based on the availability, consistency, and nature of the data this study considers five-year traffic crashes of Budapest city intersection zone of the road network to envision the aptness of the stated method. To address the goal, this study considers the minimum spacing between intersection to define the radius of output cell size (0.001093 degree) and its neighborhood radius (0.002 degree). Based on the above parameter this study identifies five common intersection zones of the road network as blackspot locations. The overall inference of this study; point density estimation is more desirable than kernel density estimation for identifying highly extreme dense blackspot locations. Furthermore, this study discovered that intersection zones with an enormous number of legs are more likely to experience high traffic crashes. This study recommends that point density estimation is capable of investigating blackspot location at macroscopic level of road network to analyze highly extreme dense traffic crash location. In addition to that, it is advisable to assess the performance of the stated blackspot location and propose remedial action to minimize the rate of road traffic crashes and its outcome.

Using the iPhone's LiDAR technology to capture 3D forensic data at crime and crash scenes

Using the iPhone's LiDAR technology to capture 3D forensic data at crime and crash scenes

Piloting a hospital-based road traffic injury surveillance system in Nairobi County, Kenya, 2018–2019

BackgroundKenya's estimated road traffic injury (RTI) death rate is 27.8/100,000 population, which is 1.5 times the global rate. Some RTI data are collected in Kenya; however, a systematic and integrated surveillance system does not exist. Therefore, we adopted and modified the World Health Organization's injury surveillance guidelines to pilot a hospital-based RTI surveillance system in Nairobi County, Kenya. MethodsWe prospectively documented all RTI cases presenting at two public trauma hospitals in Nairobi County from October 2018–April 2019. RTI cases were defined as injuries involving ≥1 moving vehicles on public roads. Demographics, injury circumstances, and outcome information were collected using standardized case report forms. The Kampala Trauma Score (KTS) was used to assess injury severity. RTI cases were characterized with descriptive statistics. ResultsOf the 1,840 RTI cases reported during the seven-month period, 73.2% were male. The median age was 29.8 years (range 1–89 years). Forty percent (n = 740) were taken to the hospital by bystanders. Median time for hospital arrival was 77 min. Pedestrians constituted 54.1% (n = 995) of cases. Of 400 motorcyclists, 48.0% lacked helmets. Similarly, 65.7% of bicyclists (23/35) lacked helmets. Among 386 motor vehicle occupants, 59.6% were not using seat belts (19.9% unknown). Seven percent of cases (n = 129) reported alcohol use (49.0% unknown), and 8.8% (n = 161) reported mobile phone use (59.7% unknown). Eleven percent of cases (n = 199) were severely injured (KTS <11), and 220 died. ConclusionWe demonstrated feasibility of a hospital-based RTI surveillance system in Nairobi County. Integrating information from crash scenes and hospitals can guide prevention.

Modeling Electric Vehicle’s Battery Module using Computational Homogenization Approach

Numerical simulations of heterogeneous structures like battery modules of electric vehicles are challenging due to the various length scales involved in it. Even with the latest computing technology, it is impossible to simulate the crash scene of the full vehicle resolving all length scales. Such hurdles have prevented manufacturers to understand the mechanical response of battery packs in vehicle crash scenarios. In this work, the problem of multiple length scales was solved using the RVE technique based on homogenization theory. An appropriate representative volume element was identified, and a 3D FE model was developed. Classical first-order boundary conditions were used in this research work. The RVE was subjected to several macroscopic deformations, and its response was obtained. The homogenized material properties were computed from the obtained responses, and a material model available in LS-Dyna’s material library was selected and calibrated to describe the nonlinear multiaxial behavior of the homogenized battery module at the macroscale. For validation, the USABC Crush and Drop Tests were simulated for the detailed and homogenized battery modules. The main output of this research is a robust and computationally efficient tool enabling satisfactory integration of a battery pack model to the vehicle for crash simulations, eliminating the need to simulate micro details at macro length scales. This approach significantly reduced the computational cost. For example, for a Drop Test simulation, the homogenized model reduced the simulation time from 40 hours (detailed model) to about 3 minutes, while maintaining a high precision ( R 2 = 0.9871 ) in predicting the load-displacement response. The system level modeling will enable the stakeholders to perform efficient optimization and safety evaluation for full-scale crashworthiness of electric vehicles.

Automated vehicle data pipeline for accident reconstruction: New insights from LiDAR, camera, and radar data

As automated vehicles are deployed across the world, it has become critically important to understand how these vehicles interact with each other, as well as with other conventional vehicles on the road. One such method to achieve a deeper understanding of the safety implications for Automated Vehicles (AVs) is to analyze instances where AVs were involved in crashes. Unfortunately, this poses a steep challenge to crash-scene investigators. It is virtually impossible to fully understand the factors that contributed to an AV involved crash without taking into account the vehicle’s perception and decision making. Furthermore, there is a tremendous amount of data that could provide insight into these crashes that is currently unused, as it also requires a deep understanding of the sensors and data management of the vehicle. To alleviate these problems, we propose a data pipeline that takes raw data from all on-board AV sensors such as LiDAR, radar, cameras, IMU’s, and GPS’s. We process this data into visual results that can be analyzed by crash scene investigators with no underlying knowledge of the vehicle’s perception system. To demonstrate the utility of this pipeline, we first analyze the latest information on AV crashes that have occurred in California and then select two crash scenarios that are analyzed in-depth using high-fidelity synthetic data generated from the automated vehicle simulator CARLA. The data visualization procedure is demonstrated on the real-world Kitti dataset by using the YOLO object detector and a monocular depth estimator called AdaBins. Depth from LIDAR is used as ground truth to calibrate and assess the effect of noise and errors in depth estimation. The visualization and data analysis from these scenarios clearly demonstrate the vast improvement in crash investigations that can be obtained from utilizing state-of-the-art sensing and perception systems used on AVs.

Alcohol drinking and driving habit particularly in Nigeria and the role forensic science could play

Alcohol drinking habit is a serious epidemic that afflicts people worldwide regardless of socioeconomic class and the country’s development or civilization. The purpose of this systematic review was to summarize research findings on drinking and driving, its implications for Nigerian society, and the role forensic science can play in drunk driving. Nigeria, in particular has seen a lot of fatal accidents attributed to alcohol but due to a lack of forensic sobriety and toxicology drug tests at the crash scene that is not yet in place in the country, the offenders are not punished for this crime. The only two African countries that have legislation on Blood Alcohol Concentration (BAC) are South Africa and Zimbabwe. Nigeria has no such legislation and does not conduct such tests in injury and fatal accident cases. The prevalence of drinking habits among Nigerians and in particular the drivers cannot be ignored. This study examined global drinking habits including its prevalence in Nigeria and more importantly understudied the need for forensic science interplay in a criminal investigation against drunk driving in the country. Drunk driving in Nigeria is a serious problem. Establishing and enforcing drunken driving laws as a criminal offense would assist the country in the reduction of road accidents, injuries, and fatalities relating to road accidents. Forensic science is a unique field that is equipped with skills and knowledge that could be useful for criminal investigation in the country including accident cases. Forensic scientists have outstanding technical skills to identify, detect, and even assist the country’s prosecutors in the administration of justice in crime investigations. Nigeria as a country will be well served if forensic scientists work in partnership with the government. Drinking and driving in addition to other crimes has become a matter of concern. The country’s police force and courts still rely mostly on eyewitness circumstantial evidence, testimonies, and suspects’ confessions. Based on this, I personally believe that the advent of Forensic science in the country’s system will assist a great deal in solving crimes and reducing fatal accidents due to drunk driving.

Young adults in motor vehicle collisions in Norway: user safety errors observed in majority of cases with severe or fatal injuries

PurposeWe performed a multidisciplinary investigation of young adults involved in motor vehicle collisions (MVCs) to elucidate injury mechanisms and the role of passive safety equipment such as seat belts and airbags.MethodsMVCs resulting in death or serious injuries to the driver or passengers aged 16–24 years in southeastern Norway during 2013–2016 were investigated upon informed consent. We assessed the crash scene, the motor vehicle (MV) interior and exterior, and analyzed data from medical records, forensic autopsies and reports from police and civil road authorities.ResultsThis study included 229 young adult occupants involved in 212 MVCs. The Maximum Abbreviated Injury Scale (MAIS) score was ≥2 in 111 occupants, of which 22 were fatalities. In 59% (65/111) of the cases with MAIS score ≥2 injuries, safety errors and occupant protection inadequacies were considered to have contributed to the injury outcome. Common errors were seatbelt non-use and misuse, carrying insecure luggage, and the seat back being too reclined. MAIS score ≥2 head/neck injuries were observed in side impacts despite correct seatbelt use, related to older MVs lacking side airbag curtains. The independent risk factors for MAIS score ≥2 injuries included not using a seatbelt, driving under the influence of alcohol or drugs, nighttime driving, side impacts, heavy collision partner, and MV deformation.ConclusionUser safety errors (not using a seatbelt, seatbelt misuse, excessive seat-back reclining, and insecure cargo) and a lack of occupant protection in older MVs resulted in young adults sustaining severe or fatal injuries in MVCs.

Ambulance dispatch prioritisation for traffic crashes using machine learning: A natural language approach

IntroductionDemand for emergency ambulances is increasing, therefore it is important that ambulance dispatch is prioritised appropriately. This means accurately identifying which incidents require a lights and sirens (L&S) response and those that do not. For traffic crashes, it can be difficult to identify the needs of patients based on bystander reports during the emergency phone call; as traffic crashes are complex events, often with multiple patients at the same crash with varying medical needs. This study aims to determine how well the text sent to paramedics en-route to the traffic crash scene by the emergency medical dispatcher (EMD), in combination with dispatch codes, can predict the need for a L&S ambulance response to traffic crashes. Methods: A retrospective cohort study was conducted using data from 2014 to 2016 traffic crashes attended by emergency ambulances in Perth, Western Australia. Machine learning algorithms were used to predict the need for a L&S response or not. The features were the Medical Priority Dispatch System (MPDS) determinant codes and EMD text. EMD text was converted for computation using natural language processing (Bag of Words approach). Machine learning algorithms were used to predict the need for a L&S response, defined as where one or more patients (a) died before hospital admission, (b) received L&S transport to hospital, or (c) had one or more high-acuity indicators (based on an a priori list of medications, interventions or observations. Results: There were 11,971 traffic crashes attended by ambulances during the study period, of which 22.3 % were retrospectively determined to have required a L&S response. The model with the highest accuracy was using an Ensemble machine learning algorithm with a score of 0.980 (95 % CI 0.976–0.984). This model predicted the need for an L&S response using both MPDS determinant codes and EMD text. Discussion: We found that a combination of EMD text and MPDS determinate codes can predict which traffic crashes do and do not require a lights and sirens ambulance response to the scene with a high degree of accuracy. Emergency medical services could deploy machine learning algorithms to improve the accuracy of dispatch to traffic crashes, which has the potential to result in improved system efficiency.

Assessment of Indiana Unmanned Aerial System Crash Scene Mapping Program

Many public safety agencies in the US have initiated a UAS-based procedure to document and map crash scenes. In addition to significantly reducing the time taken to document evidence as well as ensuring first responder safety, UAS-based mapping reduces incident clearance time and thus the likelihood of a secondary crash occurrence. There is a wide range of cameras used on these missions, but they are predominantly captured by mid-priced drones that cost in the range of $2000 to $4000. Indiana has developed a centralized processing center at Purdue University that has processed 252 crash scenes, mapped using 29 unique cameras, from 35 public agencies over the past three years. This paper includes a detailed case study that compares measurements obtained from a traditional ground-based real-time kinematic positioning base station and UAS-based photogrammetric mapping. The case study showed that UAS derived scale errors were within 0.1 ft (3 cm) of field measurements, a generally accepted threshold for public safety use cases. Further assessment was done on the 252 scenes using ground control scale error as the evaluation metric. To date, over 85% of the measurement errors were found to be within 0.1 ft (3 cm). When substantial errors are identified by the Purdue processing center, they are flagged for further dialog with the agency. In most of the cases with larger errors, the ground control distance was incorrectly measured, which is easily correctable by returning to the scene and performing new distance control measurements.

The association of crash response times and deaths at the crash scene: A cross-sectional analysis using the 2019 National Emergency Medical Service Information System.

Deaths at the crash scene (DAS) are crash deaths that occur within minutes after a crash. Rapid crash responses may reduce the occurrence of DAS. This study aims to assess the association of crash response time and DAS during the rush and nonrush hour periods by rurality/urbanicity. This single-year cross-sectional study used the 2019 National Emergency Medical Services (EMS) Information System. The outcome variable was DAS. The predictor variables were crash response measures: EMS Chute Initiation Time (ECIT) and EMS Travel Time (ETT). Age, gender, substance use, region of the body injured, and the revised trauma score were used as potential confounders. Logistic regression was used to assess the unadjusted and adjusted odds of DAS. A total of 654,675 persons were involved in EMS-activated road crash events, with 49.6% of the population exposed to crash events during the rush hour period. A total of 2,051 persons died at the crash scene. Compared to the baseline of less than 1minute, ECIT ranging from 1 to 5minutes was significantly associated with 58% (95% CI: 1.45-1.73) increased odds of DAS. Also, when compared to the baseline of less than 9minutes, ETT ranging between 9 and 18minutes was associated with 34% (95% CI: 1.22-1.47) increased odds of DAS. These patterns were consistent during the rush and nonrush hour periods and across rural and urban regions. Reducing crash response times may reduce the occurrence of DAS.